数据获取：如何在线获取的GFS雪深两周后预报

%matplotlib inline
from siphon.catalog import TDSCatalog
best_gfs = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/grib/NCEP/GFS/'
                      'Global_0p25deg/catalog.xml?dataset=grib/NCEP/GFS/Global_0p25deg/Best')
best_gfs.datasets

['Best GFS Quarter Degree Forecast Time Series']

best_ds = list(best_gfs.datasets.values())[0]
ncss = best_ds.subset()

query = ncss.query()
ncss.variables

{'Absolute_vorticity_isobaric',
 'Albedo_surface_Mixed_intervals_Average',
 'Apparent_temperature_height_above_ground',
 'Best_4_layer_Lifted_Index_surface',
 'Categorical_Freezing_Rain_surface',
 'Categorical_Freezing_Rain_surface_Mixed_intervals_Average',
 'Categorical_Ice_Pellets_surface',
 'Categorical_Ice_Pellets_surface_Mixed_intervals_Average',
 'Categorical_Rain_surface',
 'Categorical_Rain_surface_Mixed_intervals_Average',
 'Categorical_Snow_surface',
 'Categorical_Snow_surface_Mixed_intervals_Average',
 'Cloud_Work_Function_entire_atmosphere_single_layer_Mixed_intervals_Average',
 'Cloud_mixing_ratio_hybrid',
 'Cloud_mixing_ratio_isobaric',
 'Cloud_water_entire_atmosphere_single_layer',
 'Composite_reflectivity_entire_atmosphere',
 'Convective_Precipitation_Rate_surface_Mixed_intervals_Average',
 'Convective_available_potential_energy_pressure_difference_layer',
 'Convective_available_potential_energy_surface',
 'Convective_inhibition_pressure_difference_layer',
 'Convective_inhibition_surface',
 'Convective_precipitation_rate_surface',
 'Convective_precipitation_surface_Mixed_intervals_Accumulation',
 'Dewpoint_temperature_height_above_ground',
 'Downward_Long-Wave_Radp_Flux_surface_Mixed_intervals_Average',
 'Downward_Short-Wave_Radiation_Flux_surface_Mixed_intervals_Average',
 'Field_Capacity_surface',
 'Frictional_Velocity_surface',
 'Geopotential_height_cloud_ceiling',
 'Geopotential_height_highest_tropospheric_freezing',
 'Geopotential_height_isobaric',
 'Geopotential_height_maximum_wind',
 'Geopotential_height_potential_vorticity_surface',
 'Geopotential_height_surface',
 'Geopotential_height_tropopause',
 'Geopotential_height_zeroDegC_isotherm',
 'Graupel_snow_pellets_hybrid',
 'Graupel_snow_pellets_isobaric',
 'Ground_Heat_Flux_surface_Mixed_intervals_Average',
 'Haines_index_surface',
 'High_cloud_cover_high_cloud',
 'High_cloud_cover_high_cloud_Mixed_intervals_Average',
 'ICAO_Standard_Atmosphere_Reference_Height_maximum_wind',
 'ICAO_Standard_Atmosphere_Reference_Height_tropopause',
 'Ice_cover_surface',
 'Ice_growth_rate_altitude_above_msl',
 'Ice_temperature_surface',
 'Ice_thickness_surface',
 'Ice_water_mixing_ratio_hybrid',
 'Ice_water_mixing_ratio_isobaric',
 'Land_cover_0__sea_1__land_surface',
 'Latent_heat_net_flux_surface_Mixed_intervals_Average',
 'Liquid_Volumetric_Soil_Moisture_non_Frozen_depth_below_surface_layer',
 'Low_cloud_cover_low_cloud',
 'Low_cloud_cover_low_cloud_Mixed_intervals_Average',
 'MSLP_Eta_model_reduction_msl',
 'Maximum_temperature_height_above_ground_Mixed_intervals_Maximum',
 'Medium_cloud_cover_middle_cloud',
 'Medium_cloud_cover_middle_cloud_Mixed_intervals_Average',
 'Meridional_Flux_of_Gravity_Wave_Stress_surface_Mixed_intervals_Average',
 'Minimum_temperature_height_above_ground_Mixed_intervals_Minimum',
 'Momentum_flux_u-component_surface_Mixed_intervals_Average',
 'Momentum_flux_v-component_surface_Mixed_intervals_Average',
 'Ozone_Mixing_Ratio_isobaric',
 'Per_cent_frozen_precipitation_surface',
 'Planetary_Boundary_Layer_Height_surface',
 'Plant_Canopy_Surface_Water_surface',
 'Potential_Evaporation_Rate_surface',
 'Potential_temperature_sigma',
 'Precipitable_water_entire_atmosphere_single_layer',
 'Precipitation_rate_surface',
 'Precipitation_rate_surface_Mixed_intervals_Average',
 'Pressure_convective_cloud_bottom',
 'Pressure_convective_cloud_top',
 'Pressure_height_above_ground',
 'Pressure_high_cloud_bottom_Mixed_intervals_Average',
 'Pressure_high_cloud_top_Mixed_intervals_Average',
 'Pressure_low_cloud_bottom_Mixed_intervals_Average',
 'Pressure_low_cloud_top_Mixed_intervals_Average',
 'Pressure_maximum_wind',
 'Pressure_middle_cloud_bottom_Mixed_intervals_Average',
 'Pressure_middle_cloud_top_Mixed_intervals_Average',
 'Pressure_of_level_from_which_parcel_was_lifted_pressure_difference_layer',
 'Pressure_potential_vorticity_surface',
 'Pressure_reduced_to_MSL_msl',
 'Pressure_surface',
 'Pressure_tropopause',
 'Rain_mixing_ratio_hybrid',
 'Rain_mixing_ratio_isobaric',
 'Reflectivity_height_above_ground',
 'Reflectivity_hybrid',
 'Relative_humidity_entire_atmosphere_single_layer',
 'Relative_humidity_height_above_ground',
 'Relative_humidity_highest_tropospheric_freezing',
 'Relative_humidity_isobaric',
 'Relative_humidity_pressure_difference_layer',
 'Relative_humidity_sigma',
 'Relative_humidity_sigma_layer',
 'Relative_humidity_zeroDegC_isotherm',
 'Sensible_heat_net_flux_surface_Mixed_intervals_Average',
 'Snow_depth_surface',
 'Snow_mixing_ratio_hybrid',
 'Snow_mixing_ratio_isobaric',
 'Soil_temperature_depth_below_surface_layer',
 'Soil_type_surface',
 'Specific_humidity_height_above_ground',
 'Specific_humidity_isobaric',
 'Specific_humidity_pressure_difference_layer',
 'Storm_relative_helicity_height_above_ground_layer',
 'Sunshine_Duration_surface',
 'Surface_Lifted_Index_surface',
 'Surface_roughness_surface',
 'Temperature_altitude_above_msl',
 'Temperature_height_above_ground',
 'Temperature_high_cloud_top_Mixed_intervals_Average',
 'Temperature_isobaric',
 'Temperature_low_cloud_top_Mixed_intervals_Average',
 'Temperature_maximum_wind',
 'Temperature_middle_cloud_top_Mixed_intervals_Average',
 'Temperature_potential_vorticity_surface',
 'Temperature_pressure_difference_layer',
 'Temperature_sigma',
 'Temperature_surface',
 'Temperature_tropopause',
 'Total_cloud_cover_boundary_layer_cloud_Mixed_intervals_Average',
 'Total_cloud_cover_convective_cloud',
 'Total_cloud_cover_entire_atmosphere',
 'Total_cloud_cover_entire_atmosphere_Mixed_intervals_Average',
 'Total_cloud_cover_isobaric',
 'Total_ozone_entire_atmosphere_single_layer',
 'Total_precipitation_surface_Mixed_intervals_Accumulation',
 'U-Component_Storm_Motion_height_above_ground_layer',
 'Upward_Long-Wave_Radp_Flux_atmosphere_top_Mixed_intervals_Average',
 'Upward_Long-Wave_Radp_Flux_surface_Mixed_intervals_Average',
 'Upward_Short-Wave_Radiation_Flux_atmosphere_top_Mixed_intervals_Average',
 'Upward_Short-Wave_Radiation_Flux_surface_Mixed_intervals_Average',
 'V-Component_Storm_Motion_height_above_ground_layer',
 'Vegetation_surface',
 'Ventilation_Rate_planetary_boundary',
 'Vertical_Speed_Shear_potential_vorticity_surface',
 'Vertical_Speed_Shear_tropopause',
 'Vertical_velocity_geometric_isobaric',
 'Vertical_velocity_pressure_isobaric',
 'Vertical_velocity_pressure_sigma',
 'Visibility_surface',
 'Volumetric_Soil_Moisture_Content_depth_below_surface_layer',
 'Water_equivalent_of_accumulated_snow_depth_surface',
 'Water_runoff_surface_Mixed_intervals_Accumulation',
 'Wilting_Point_surface',
 'Wind_speed_gust_surface',
 'Zonal_Flux_of_Gravity_Wave_Stress_surface_Mixed_intervals_Average',
 'u-component_of_wind_altitude_above_msl',
 'u-component_of_wind_height_above_ground',
 'u-component_of_wind_isobaric',
 'u-component_of_wind_maximum_wind',
 'u-component_of_wind_planetary_boundary',
 'u-component_of_wind_potential_vorticity_surface',
 'u-component_of_wind_pressure_difference_layer',
 'u-component_of_wind_sigma',
 'u-component_of_wind_tropopause',
 'v-component_of_wind_altitude_above_msl',
 'v-component_of_wind_height_above_ground',
 'v-component_of_wind_isobaric',
 'v-component_of_wind_maximum_wind',
 'v-component_of_wind_planetary_boundary',
 'v-component_of_wind_potential_vorticity_surface',
 'v-component_of_wind_pressure_difference_layer',
 'v-component_of_wind_sigma',
 'v-component_of_wind_tropopause'}

from datetime import datetime
query.lonlat_box(north=50, south=20, east=130, west=100).time(datetime(2022, 2, 18, 18))
query.accept('netcdf4')
query.variables('Temperature_surface')
data = ncss.get_data(query)

---------------------------------------------------------------------------
HTTPError                                 Traceback (most recent call last)
<ipython-input-4-8830d7806601> in <module>
      3 query.accept('netcdf4')
      4 query.variables('Temperature_surface')
----> 5 data = ncss.get_data(query)

/opt/conda/lib/python3.9/site-packages/siphon/ncss.py in get_data(self, query)
    112 
    113         """
--> 114         resp = self.get_query(query)
    115         return response_handlers(resp, self.unit_handler)
    116 

/opt/conda/lib/python3.9/site-packages/siphon/http_util.py in get_query(self, query)
    408         """
    409         url = self._base[:-1] if self._base[-1] == '/' else self._base
--> 410         return self.get(url, query)
    411 
    412     def url_path(self, path):

/opt/conda/lib/python3.9/site-packages/siphon/http_util.py in get(self, path, params)
    490             else:
    491                 text = resp.text
--> 492             raise requests.HTTPError('Error accessing {0}\n'
    493                                      'Server Error ({1:d}: {2})'.format(resp.request.url,
    494                                                                         resp.status_code,

HTTPError: Error accessing https://thredds.ucar.edu/thredds/ncss/grid/grib/NCEP/GFS/Global_0p25deg/Best?var=Temperature_surface&time=2022-02-18T18%3A00%3A00&west=100&east=130&south=20&north=50&accept=netcdf4
Server Error (400: Requested time 2022-02-18T18:00:00Z does not intersect actual time range 2024-02-07T00:00:00Z - 2024-03-01T06:00:00Z)

from datetime import datetime
query.lonlat_box(north=70, south=20, east=130, west=100).time(datetime(2024, 3, 1, 6))
query.accept('netcdf4')
query.variables('Snow_depth_surface')
data = ncss.get_data(query)

ds = ncss.get_data(query)
list(ds.variables)

['reftime',
 'latitude',
 'time',
 'longitude',
 'Snow_depth_surface',
 'LatLon_721X1440-0p13S-180p00E-2']

from netCDF4 import num2date
import numpy as np
snow_var = data.variables['Snow_depth_surface']

# Time variables can be renamed in GRIB collections. Best to just pull it out of the

time_name = snow_var.coordinates.split()[1]
time_var = data.variables[time_name]
lat_var = data.variables['latitude']
lon_var = data.variables['longitude']
# Get the actual data values and remove any size 1 dimensions
snow_vals = snow_var[:].squeeze()
lat_vals = lat_var[:].squeeze()
lon_vals = lon_var[:].squeeze()

# Convert the number of hours since the reference time to an actual date
time_val = num2date(time_var[:].squeeze(), time_var.units)


# Combine 1D latitude and longitudes into a 2D grid of locations
lon_2d, lat_2d = np.meshgrid(lon_vals, lat_vals)

import cmaps
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
# Create a new figure with proper size
fig = plt.figure(figsize=(15, 12))

# Add the map and set the extent
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
ax.set_extent([100., 130., 23, 70])

# Add coastlines and country borders
ax.coastlines()

# Contour temperature at each lat/lon
cf = ax.contourf(lon_2d, lat_2d, snow_vals, 200, transform=ccrs.PlateCarree(), cmap=cmaps.precip4_11lev)

# Plot a colorbar to show temperature and reduce the size of it
plt.colorbar(cf, ax=ax, fraction=0.032)

# Make a title with the time value
ax.set_title('Snow_depth_surface for {:%d %B %Y %H:%MZ}'.format(time_val), fontsize=20)

# Plot markers for each lat/long to show grid points for 0.25 deg GFS
ax.plot(lon_2d.flatten(), lat_2d.flatten(), marker='o', color='black', markersize=2,
        alpha=0.3, transform=ccrs.Geodetic(), linestyle='none')

plt.show()